Our AI risk models stop fraud before the money leaves the bank. Discover examples of the fraud cases we’ve successfully prevented across the globe. These include BEC fraud, social engineering, scams, CEO fraud, fake invoices, account takeover, authorized push payment, internal fraud, and many more.
Switzerland
The fraudster used a pharming technique, injecting malicious code into the victim’s computer, to acquire a customer’s e-banking credentials. The fraudster was then able to take over the customer’s account to seize a payment of CHF 19,990.
NetGuardians stopped the payment as a number of factors didn’t match the customer’s profile, including the new beneficiary (both at the customer and bank level), as well as the unusual screen resolution, session browser, and the amount.
Switzerland
The fraudster impersonated a bank customer and asked the banking employee to order a transaction of CHF 1,000,000. The banking employee was scammed, believed that the fraudster was the customer and validated the transaction.
Although the case involved a new client with little historical data, NetGuardians blocked the payment because the transaction was unusual at the bank level, and the beneficiary account was also unusual.
The Netherlands
The customer, a victim of vishing, gave his/her e-banking credentials over the phone to the fraudster posing as an agent. The fraudster then took over their e-banking and transferred CHF 22,114 to an illicit account.
Although the case involved a new customer with little historical data, NetGuardians’ AI risk models stopped the fraud because the transaction characteristics were unusual at the bank level, as was the screen resolution.
The Netherlands
The fraudster impersonated a bank agent and through social engineering persuaded the customer to release his/her e-banking login details. The fraudster took over the customer’s account and made a payment of CHF 33,170 to an account of their own.
NetGuardians’ AI risk models identified the transaction as fraudulent and blocked it because the e-banking metrics – e-banking language, browser, screen resolution, etc – did not match the customer’s usual profile.
Lithuania
The fraudster impersonated a Microsoft tech support worker and called the victim. Through social engineering, the fraudster managed to obtain enough information about the victim’s e-banking to transfer USD 7,500 to an illicit account.
NetGuardians’ AI risk models stopped the transaction due to its variables not matching the customer’s profile, noting the unusual currency, operation type, beneficiary account, beneficiary country, account ID, and account type.
Great Britain
A fraudster impersonating a banking agent persuaded a customer to disclose his/her e-banking login details through social engineering, took over the account and attempted a payment of GBP 21,000 to an illicit account.
NetGuardians’ AI risk models blocked the transaction due to unusual e-banking and transaction variables, including unusual amount, screen resolution, beneficiary institution, beneficiary account, e-banking session language, and currency.
Spain
Using social engineering techniques, the fraudster convinced the bank customer to transfer EUR 125,000 to an illicit account.
NetGuardians’ AI risk models stopped the transaction as certain variables did not match the customer’s profile, including the transaction creation day, the beneficiary account, beneficiary country, operation type, order type, and currency.
Spain
The fraudster impersonated the CEO of the account holder’s company and over email convinced the victim to pay EUR 170,000 into an illicit account.
NetGuardians’ AI risk models stopped the transaction due to unusual variables including the beneficiary account and country, the operation type, order type, and currency.
Peru
A company employee fell victim to a business email compromise (BEC) fraud after their business partner’s email account was hacked and the fraudster sent a fake email from this account requesting a payment of USD 100,000 to a beneficiary in Peru.
NetGuardians’ AI risk models stopped the transaction due to the unusual destination country, payment creation date, and beneficiary account.
Bulgaria
The victim was advised by a fraudster impersonating a business partner to invest in a fictitious company and ordered a payment of USD 170,000.
NetGuardians stopped the transaction due to variables not matching the customer’s profile, including the unusual destination, beneficiary bank, beneficiary account, amount, and currency.
Belgium
A customer fell victim to social engineering and was scammed on the purchase of a car. When the customer tried to pay a non-existent car company, NetGuardians solution stopped the payment before the money had left the bank.
NetGuardians blocked the transaction because of a number of unusual variables. These included an unusual beneficiary for the customer and the bank as well as the unusual amount and destination country.
Germany
The fraudster introduced himself to the victim as an American soldier based in the Middle East. A romantic relationship started and the fraudster convinced the victim to make three transactions to his bank in Germany – of USD 1,500, of EUR 3,000, and one of EUR 11,300.
NetGuardians’ AI risk models stopped the first and third transactions, spotting unusual variables including the beneficiary bank account, the destination bank country, the amount and currency.
Kenya
Fraudsters manipulated internal accounts in one bank to send money to another bank, who are protected by NetGuardians. The fraud was detected in real time and the fraudsters were arrested while attempting to withdraw USD 500,000 (KSh 58 million) from the second bank’s ATMs.
NetGuardians AI risk models prevented the fraud due to the unusual timing and frequency of ATM withdrawals. After the detection of the fraud scheme by our solution, fraudsters were successfully caught by the police using the cameras installed at the ATM’s.
Slovenia
The fraudster impersonated a Microsoft employee and called the victim to say he had been hacked. The victim gave over control of his computer to the fraudster, who then planted malicious code and was able to transfer a payment of EUR 3,469 from the victim’s bank account to an illicit account in Slovenia.
NetGuardians spotted a number of unusual transaction variables and stopped the payment. These included an unusual beneficiary country and account, and unusual currency for the customer, and an unusual beneficiary account for the bank.
Italy
NetGuardians software spotted a number of unusual attributes around the transaction, including the unusual amount, beneficiary country, beneficiary account, order type, operation type, and currency.
Tanzania
An IT administrator with back-end user privileges inflated account balances for an accomplice. The funds were then were ready to be withdrawn from ATMs and mobile banking but we detected the fraud before the money left the bank.
NetGuardians’ AI risk models spotted the internal fraud because the privileged user checked the account several times over a number of days, behavior the models picked up as suspicious.
Philippines
A rogue employee exploited a misconfiguration of the core-banking system that allowed direct debit transactions to be posted on dormant accounts. The fraudster then attempted to withdraw the funds.
NetGuardians’ AI risk models spotted the fraud because the transactions did not match the customers’ historical profiles.
Singapore
The victim received an invitation via a phishing email apparently from the government containing a malicious link. On clicking the link, the fraudsters were able to install a banking Trojan, add a new device, enter two payments and validate them through eBanking.
NetGuardians’ AI risk models detected and stopped the payments after spotting variables that didn’t match the victim’s usual profile. These included the unusual screen resolution, beneficiary institution, beneficiary account, amount of transaction and browser.
Malaysia
A bank customer received a call from someone impersonating a Microsoft employee. The fraudster convinced the victim to sign into his eBanking and subsequently took control of the account, initiating a EUR 18’000 payment to an account in Malaysia.
NetGuardians software detected and stopped the payment because a number of factors didn’t match the customer’s profile including the currency, the beneficiary, operation type and the beneficiary country.
Germany
A client wanted to access her eBanking service. After entering her credentials and scanning the QR code, a message appeared in French and German asking her to re-enter her credentials for greater security. After she did so, an error message appeared saying the site was unavailable. This is likely to have been a man in the middle attack in which the second QR code was displayed by a hacker to recover the victim’s account credentials. The fraudster then attempted a payment of CHF 38,000.
NetGuardians blocked the payment due to unusual session information, including browser language and screen resolution, and transaction details including the unusual amount and beneficiary bank. The system logs showed three sessions that raised suspicions, suggesting the fraudster accessed the victim’s account several times while attempting the payment.
United States
A fraudster impersonating tax authorities, phoned the victim to say she needed to pay a large amount of money for undeclared funds. Threatened over the phone and under pressure, she agreed to initiate a transfer of USD 130,000 to an account named by the fraudster.
NetGuardians’ stopped the payment because of a number of factors outside the customer’s payment profile. These included the unusual beneficiary account, both for the customer and the bank, and the unusual amount.
United States
A bank customer received emails from a fraudster impersonating an existing supplier which instructed him to change the IBAN of the beneficiary for certain transactions as the supplier’s account details had changed. The customer proceeded with 8 payments without any further control or validation, sending a total of USD 850,000 to the fraudster’s IBAN account.
NetGuardians’ AI solution stopped the fraudulent payments as there were unusual factors such as unusual beneficiary account and unusual country.
